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Author Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera
Title Support Vector Machines with Time Series Distance Kernels for Action Classification Type Conference Article
Year 2016 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal
Volume Issue Pages 1-7
Keywords (down)
Abstract Despite the outperformance of Support Vector Machine (SVM) on many practical classification problems, the algorithm is not directly applicable to multi-dimensional trajectories having different lengths. In this paper, a new class of SVM that is applicable to trajectory classification, such as action recognition, is developed by incorporating two efficient time-series distances measures into the kernel function.
Dynamic Time Warping and Longest Common Subsequence distance measures along with their derivatives are
employed as the SVM kernel. In addition, the pairwise proximity learning strategy is utilized in order to make use of non-positive semi-definite kernels in the SVM formulation. The proposed method is employed for a challenging classification problem: action recognition by depth cameras using only skeleton data; and evaluated on three benchmark action datasets. Experimental results demonstrate the outperformance of our methodology compared to the state-ofthe-art on the considered datasets.
Address Lake Placid; NY (USA); March 2016
Corporate Author Thesis
Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference WACV
Notes HuPBA;MILAB; Approved no
Call Number Admin @ si @ BGE2016a Serial 2773
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Author Gloria Fernandez Esparrach; Jorge Bernal; Maria Lopez Ceron; Henry Cordova; Cristina Sanchez Montes; Cristina Rodriguez de Miguel; F. Javier Sanchez
Title Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps Type Journal Article
Year 2016 Publication Endoscopy Abbreviated Journal END
Volume 48 Issue 9 Pages 837-842
Keywords (down)
Abstract Background and aims: Polyp miss-rate is a drawback of colonoscopy that increases significantly in small polyps. We explored the efficacy of an automatic computer vision method for polyp detection.
Methods: Our method relies on a model that defines polyp boundaries as valleys of image intensity. Valley information is integrated into energy maps which represent the likelihood of polyp presence.
Results: In 24 videos containing polyps from routine colonoscopies, all polyps were detected in at least one frame. Mean values of the maximum of energy map were higher in frames with polyps than without (p<0.001). Performance improved in high quality frames (AUC= 0.79, 95%CI: 0.70-0.87 vs 0.75, 95%CI: 0.66-0.83). Using 3.75 as maximum threshold value, sensitivity and specificity for detection of polyps were 70.4% (95%CI: 60.3-80.8) and 72.4% (95%CI: 61.6-84.6), respectively.
Conclusion: Energy maps showed a good performance for colonic polyp detection. This indicates a potential applicability in clinical practice.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MV; Approved no
Call Number Admin @ si @FBL2016 Serial 2778
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Author Gloria Fernandez Esparrach; Jorge Bernal; Cristina Rodriguez de Miguel; Debora Gil; Fernando Vilariño; Henry Cordova; Cristina Sanchez Montes; Isis Ara
Title Utilidad de la visión por computador para la localización de pólipos pequeños y planos Type Conference Article
Year 2016 Publication XIX Reunión Nacional de la Asociación Española de Gastroenterología, Gastroenterology Hepatology Abbreviated Journal
Volume 39 Issue 2 Pages 94
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Abstract
Address Madrid (Spain)
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference AEGASTRO
Notes MV; IAM; 600.097;SIAI Approved no
Call Number Admin @ si @FBR2016 Serial 2779
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Author Francesco Ciompi; Simone Balocco; Juan Rigla; Xavier Carrillo; J. Mauri; Petia Radeva
Title Computer-Aided Detection of Intra-Coronary Stent in Intravascular Ultrasound Sequences Type Journal Article
Year 2016 Publication Medical Physics Abbreviated Journal MP
Volume 43 Issue 10 Pages
Keywords (down)
Abstract Purpose: An intraluminal coronary stent is a metal mesh tube deployed in a stenotic artery during Percutaneous Coronary Intervention (PCI), in order to prevent acute vessel occlusion. The identication of struts location and the denition of the stent shape are relevant for PCI planning 15 and for patient follow-up. We present a fully-automatic framework for Computer-Aided Detection
(CAD) of intra-coronary stents in Intravascular Ultrasound (IVUS) image sequences. The CAD system is able to detect stent struts and estimate the stent shape.

Methods: The proposed CAD uses machine learning to provide a comprehensive interpretation of the local structure of the vessel by means of semantic classication. The output of the classication 20 stage is then used to detect struts and to estimate the stent shape. The proposed approach is validated using a multi-centric data-set of 1,015 images from 107 IVUS sequences containing both metallic and bio-absorbable stents.

Results: The method was able to detect structs in both metallic stents with an overall F-measure of 77.7% and a mean distance of 0.15 mm from manually annotated struts, and in bio-absorbable 25 stents with an overall F-measure of 77.4% and a mean distance of 0.09 mm from manually annotated struts.

Conclusions: The results are close to the inter-observer variability and suggest that the system has the potential of being used as method for aiding percutaneous interventions.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes MILAB Approved no
Call Number Admin @ si @ CBR2016 Serial 2819
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Author Maria Oliver; Gloria Haro; Mariella Dimiccoli; Baptiste Mazin; Coloma Ballester
Title A computational model of amodal completion Type Conference Article
Year 2016 Publication SIAM Conference on Imaging Science Abbreviated Journal
Volume Issue Pages
Keywords (down)
Abstract This paper presents a computational model to recover the most likely interpretation of the 3D scene structure from a planar image, where some objects may occlude others. The estimated scene interpretation is obtained by integrating some global and local cues and provides both the complete disoccluded objects that form the scene and their ordering according to depth. Our method first computes several distal scenes which are compatible with the proximal planar image. To compute these different hypothesized scenes, we propose a perceptually inspired object disocclusion method, which works by minimizing the Euler's elastica as well as by incorporating the relatability of partially occluded contours and the convexity of the disoccluded objects. Then, to estimate the preferred scene we rely on a Bayesian model and define probabilities taking into account the global complexity of the objects in the hypothesized scenes as well as the effort of bringing these objects in their relative position in the planar image, which is also measured by an Euler's elastica-based quantity. The model is illustrated with numerical experiments on, both, synthetic and real images showing the ability of our model to reconstruct the occluded objects and the preferred perceptual order among them. We also present results on images of the Berkeley dataset with provided figure-ground ground-truth labeling.
Address Albuquerque; New Mexico; USA; May 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference IS
Notes MILAB; 601.235 Approved no
Call Number Admin @ si @OHD2016a Serial 2788
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Author G. de Oliveira; A. Cartas; Marc Bolaños; Mariella Dimiccoli; Xavier Giro; Petia Radeva
Title LEMoRe: A Lifelog Engine for Moments Retrieval at the NTCIR-Lifelog LSAT Task Type Conference Article
Year 2016 Publication 12th NTCIR Conference on Evaluation of Information Access Technologies Abbreviated Journal
Volume Issue Pages
Keywords (down)
Abstract Semantic image retrieval from large amounts of egocentric visual data requires to leverage powerful techniques for filling in the semantic gap. This paper introduces LEMoRe, a Lifelog Engine for Moments Retrieval, developed in the context of the Lifelog Semantic Access Task (LSAT) of the the NTCIR-12 challenge and discusses its performance variation on different trials. LEMoRe integrates classical image descriptors with high-level semantic concepts extracted by Convolutional Neural Networks (CNN), powered by a graphic user interface that uses natural language processing. Although this is just a first attempt towards interactive image retrieval from large egocentric datasets and there is a large room for improvement of the system components and the user interface, the structure of the system itself and the way the single components cooperate are very promising.
Address Tokyo; Japan; June 2016
Corporate Author Thesis
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Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference NTCIR
Notes MILAB; Approved no
Call Number Admin @ si @OCB2016 Serial 2789
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Author G. de Oliveira; Mariella Dimiccoli; Petia Radeva
Title Egocentric Image Retrieval With Deep Convolutional Neural Networks Type Conference Article
Year 2016 Publication 19th International Conference of the Catalan Association for Artificial Intelligence Abbreviated Journal
Volume Issue Pages 71-76
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Abstract
Address Barcelona; Spain; October 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CCIA
Notes MILAB Approved no
Call Number Admin @ si @ODR2016 Serial 2790
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Author Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva
Title With whom do I interact with? Social interaction detection in egocentric photo-streams Type Conference Article
Year 2016 Publication 23rd International Conference on Pattern Recognition Abbreviated Journal
Volume Issue Pages
Keywords (down)
Abstract Given a user wearing a low frame rate wearable camera during a day, this work aims to automatically detect the moments when the user gets engaged into a social interaction solely by reviewing the automatically captured photos by the worn camera. The proposed method, inspired by the sociological concept of F-formation, exploits distance and orientation of the appearing individuals -with respect to the user- in the scene from a bird-view perspective. As a result, the interaction pattern over the sequence can be understood as a two-dimensional time series that corresponds to the temporal evolution of the distance and orientation features over time. A Long-Short Term Memory-based Recurrent Neural Network is then trained to classify each time series. Experimental evaluation over a dataset of 30.000 images has shown promising results on the proposed method for social interaction detection in egocentric photo-streams.
Address Cancun; Mexico; December 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICPR
Notes MILAB Approved no
Call Number Admin @ si @ADR2016a Serial 2791
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Author Fernando Vilariño; Dan Norton; Onur Ferhat
Title The Eye Doesn't Click – Eyetracking and Digital Content Interaction Type Conference Article
Year 2016 Publication 4S/EASST Conference Abbreviated Journal
Volume Issue Pages
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Abstract
Address Barcelona; Spain; September 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference EASST
Notes MV; 600.097;SIAI Approved no
Call Number Admin @ si @VNF2016 Serial 2801
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Author Fernando Vilariño
Title Giving Value to digital collections in the Public Library Type Conference Article
Year 2016 Publication Librarian 2020 Abbreviated Journal
Volume Issue Pages
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Abstract
Address Brussels; Belgium; October 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference LIB
Notes MV; 600.097;SIAI Approved no
Call Number Admin @ si @Vil2016a Serial 2802
Permanent link to this record
 
 
Author Fernando Vilariño; Dimosthenis Karatzas
Title A Living Lab approach for Citizen Science in Libraries Type Conference Article
Year 2016 Publication 1st International ECSA Conference Abbreviated Journal
Volume Issue Pages
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Abstract
Address Berlin; Germany; May 2016
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Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference ECSA
Notes MV; DAG; 600.084; 600.097;SIAI Approved no
Call Number Admin @ si @ViK2016 Serial 2804
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Author Fernando Vilariño
Title Dissemination, creation and education from archives: Case study of the collection of Digitized Visual Poems from Joan Brossa Foundation Type Conference Article
Year 2016 Publication International Workshop on Poetry: Archives, Poetries and Receptions Abbreviated Journal
Volume Issue Pages
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Abstract
Address Barcelona; Spain; October 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference POETRY
Notes MV; 600.097;SIAI Approved no
Call Number Admin @ si @Vil2016b Serial 2805
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Author Cristhian A. Aguilera-Carrasco; F. Aguilera; Angel Sappa; C. Aguilera; Ricardo Toledo
Title Learning cross-spectral similarity measures with deep convolutional neural networks Type Conference Article
Year 2016 Publication 29th IEEE Conference on Computer Vision and Pattern Recognition Worshops Abbreviated Journal
Volume Issue Pages
Keywords (down)
Abstract The simultaneous use of images from different spectracan be helpful to improve the performance of many computer vision tasks. The core idea behind the usage of crossspectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN architectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Experimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Additionally, our experiments show that some CNN architectures are capable of generalizing between different crossspectral domains.
Address Las vegas; USA; June 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CVPRW
Notes ADAS; 600.086; 600.076 Approved no
Call Number Admin @ si @AAS2016 Serial 2809
Permanent link to this record
 
 
Author Jose A. Garcia; David Masip; Valerio Sbragaglia; Jacopo Aguzzi
Title Using ORB, BoW and SVM to identificate and track tagged Norway lobster Nephrops Norvegicus (L.) Type Conference Article
Year 2016 Publication 3rd International Conference on Maritime Technology and Engineering Abbreviated Journal
Volume Issue Pages
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Abstract Sustainable capture policies of many species strongly depend on the understanding of their social behaviour. Nevertheless, the analysis of emergent behaviour in marine species poses several challenges. Usually animals are captured and observed in tanks, and their behaviour is inferred from their dynamics and interactions. Therefore, researchers must deal with thousands of hours of video data. Without loss of generality, this paper proposes a computer
vision approach to identify and track specific species, the Norway lobster, Nephrops norvegicus. We propose an identification scheme were animals are marked using black and white tags with a geometric shape in the center (holed
triangle, filled triangle, holed circle and filled circle). Using a massive labelled dataset; we extract local features based on the ORB descriptor. These features are a posteriori clustered, and we construct a Bag of Visual Words feature vector per animal. This approximation yields us invariance to rotation
and translation. A SVM classifier achieves generalization results above 99%. In a second contribution, we will make the code and training data publically available.
Address Lisboa; Portugal; July 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference MARTECH
Notes OR;MV; Approved no
Call Number Admin @ si @ GMS2016b Serial 2817
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Author Vassileios Balntas; Edgar Riba; Daniel Ponsa; Krystian Mikolajczyk
Title Learning local feature descriptors with triplets and shallow convolutional neural networks Type Conference Article
Year 2016 Publication 27th British Machine Vision Conference Abbreviated Journal
Volume Issue Pages
Keywords (down)
Abstract It has recently been demonstrated that local feature descriptors based on convolutional neural networks (CNN) can significantly improve the matching performance. Previous work on learning such descriptors has focused on exploiting pairs of positive and negative patches to learn discriminative CNN representations. In this work, we propose to utilize triplets of training samples, together with in-triplet mining of hard negatives.
We show that our method achieves state of the art results, without the computational overhead typically associated with mining of negatives and with lower complexity of the network architecture. We compare our approach to recently introduced convolutional local feature descriptors, and demonstrate the advantages of the proposed methods in terms of performance and speed. We also examine different loss functions associated with triplets.
Address York; UK; September 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference BMVC
Notes ADAS; 600.086 Approved no
Call Number Admin @ si @ BRP2016 Serial 2818
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